Counterfactual Explanations (CEs): CEs is a branch of post-hoc explanations. They are relevant to legally justify decisions made automatically by algorithms [1]}. In a nutshell, a CE is the smallest meaningful change to an input sample to obtain a desirable outcome of the algorithm. Some recent methods [2]}, [3]} exploit the query image's regions and a different classified picture to interchange semantic appearances, creating counterfactual examples. Other works [1]}, [5]} leverage the input image's gradients with respect to the target label to create meaningful perturbations. Conversely, [6]} find patterns via prototypes that the image must contain to alter its prediction. Similarly, [7]}, [8]} follow a prototype-based algorithm to generate the explanations. Even Deep Image Priors [9]} and Invertible CNNs [10]} have shown the capacity to produce counterfactual examples. Furthermore, theoretical analyses [11]} found similarities between counterfactual explanations and adversarial attacks.
